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Record W4323351334 · doi:10.1093/jcag/gwac036.155

A155 OUTCOMES FOLLOWING ENDOSCOPIC RESECTION OF DUODENAL NEUROENDOCRINE TUMOURS FROM A TERTIARY-CARE ACADEMIC CENTRE

2023· article· en· W4323351334 on OpenAlexaffabout
Shagun Gupta, Gurmun Singh Brar, Kehui Zheng, Shaheed W. Hakim, C W Teshima, G R May, Calvin Law, Julie Hallet, J D Mosko

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2023
Typearticle
Languageen
FieldMedicine
TopicNeuroendocrine Tumor Research Advances
Canadian institutionsSunnybrook Health Science CentreSt. Michael's HospitalHealth Sciences CentreSt Joseph's Health CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineEndoscopic mucosal resectionEndoscopyNeuroendocrine tumorsRetrospective cohort studyDuodenumTherapeutic endoscopyIncidence (geometry)CohortSurgeryDissection (medical)Internal medicine

Abstract

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Abstract Background Duodenal neuroendocrine tumours (D-NET) are rare cancers derived from neuroendocrine cells of the duodenum. A steady increase in the incidence of these tumours has been observed. Current treatment and surveillance strategies are guided by various tumour characteristics including size, grade, and depth of invasion. There exists conflicting evidence, however, on the rates of recurrence after positive resection margins following endoscopic resection. Thus, it remains uncertain whether complete endoscopic resection (R0) of these indolent tumours is clinically significant and whether follow-up endoscopic or surgical intervention is justified. Purpose Our aim is to characterize endoscopic management and clinical outcomes in patients undergoing endoscopic resection of D-NETs. Method We conducted a retrospective, single-centre cohort study at The Centre for Advanced Therapeutic Endoscopy and Endoscopic Oncology at St. Michael’s Hospital, Toronto, Ontario. Consecutive patients over the age of 18 who underwent endoscopic resection of histologically proven D-NETs between 2011 and 2020 were included. Data on patient, endoscopic, and tumour characteristics were collected through electronic chart review. Descriptive statistics were conducted for data analysis. Result(s) A total of 155 foregut neuroendocrine tumours (NET) were endoscopically resected amongst 96 patients during the study period. 47 of these were histologically identified as D-NETs. Mean tumour size was 9.88 ± 6.86 mm. Conventional endoscopic mucosal resection (EMR) was performed most frequently (55%, n=26/47), followed by cap-assisted EMR (30%, n=14/47). Hybrid endoscopic submucosal dissection (ESD)/EMR was performed in one case. A total of two intra-procedural perforations occurred, both of which were successfully closed endoscopically. One patient with a peri-ampullary D-NET experienced significant intra-procedural bleeding requiring Hemospray® and subsequent endotracheal intubation resulting in a brief hospitalization. 57% of all resected D-NETs were followed at surveillance endoscopy 1 (SE1) at a median interval of 199 days (range, 84 to 830). Positive resection margins (R1) were found in 26 cases (55%), of which 16 were assessed at SE1 while nine were lost to follow-up. One patient with R1 margins was electively treated with APC at SE1. Tumour recurrence at SE1 occurred in only two patients. Image Conclusion(s) D-NET recurrence is found in less than 5% of patients at surveillance endoscopy following endoscopic resection in spite of a high R1 resection rate. Given this indolent nature of these tumours, our study suggests that patients with positive resection margins can be followed conservatively with surveillance endoscopy. Further investigation is warranted to determine the optimal duration and surveillance strategy for these patients. Please acknowledge all funding agencies by checking the applicable boxes below CAG Disclosure of Interest None Declared

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.292
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

Explore more

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